A Self-Government Particle Swarm Optimization Algorithm and Its Application in Texaco Gasification

In this paper, a self-government particle swarm optimizer (SGPSO) is proposed to improve the performance of original PSO, in which particle updating depends on local best information searched at anterior runs as well as individual history best and global best at present. To evaluate the novel algorithm, some benchmark functions are employed in comparison with PSO. Experimental results show that the proposed algorithm can search more optimal solution than PSO and indicate the effectiveness of the novel algorithm to solve optimization problems. Finally, the proposed algorithm is applied in soft-sensing the Texaco furnace temperature. It is convinced that SGPSO based soft sensor is very capable of real-time assessment of the furnace temperature in the Texaco gasification process.